The Value of the Human Mind - How Machine Learning is Helping Humans Win

Overview

There is no doubt that we are living in the AI era. Artificial intelligence is at work all around us today. Even if we do not realize it, our thoughts and actions are training the technology to respond in the way we desire. Machine learning is one of the fundamental tasks of AI. Just as the name implies, the machines and platforms we use daily are learning from the consistent input we provide. Let’s look into ways that machine learning is helping to make our lives much easier.

What is Machine Learning?

Machine learning is an AI component that uses algorithms to find and apply data patterns. The process involves the input of data into a model that is used to predict an outcome. The more data that is input into the model, the “smarter” the machine application seems to get.

The data used can take on many forms, such as text numbers, images, videos, clicks, etc. If there is a way that the item can be stored, it can be applied to a machine-learning model. There are a variety of ways that machine-learning algorithms are incorporated. These various algorithms fit under three specific types of machine learning.

Types of Machine Learning

The three types of machine learning are supervised learning, unsupervised learning, and reinforcement learning. Deep learning is the refined form of machine learning we see used daily. Deep learning uses an algorithm to create what are called neural networks. Neural networks are loosely based on the neural networks of the human brain. Each of these types of neural networks fits in one of the three categories.

Supervised Learning

Supervised learning involves machine learning, where variables—called features—and labels are assigned to the model used. These features and labels are utilized to properly classify the data received. The algorithm can identify patterns based on predetermined features and labels.

An example of machine learning using the supervised model is a machine that can count coins of different denominations. If the weights (features) of nickels, dimes, and quarters (labels) are input into an algorithm, the model can predict the denominations of the coins based on knowing the weight (feature) of each. Another example of this is with a music streaming service that predicts the best choices of music to play based on the genre you routinely choose.

Unsupervised Learning

Unsupervised learning does not use predetermined features and labels. The model is set up to search for any patterns it can recognize. The process is much like a person collecting shells at the beach and later categorizing them based on their shapes. Since there are no labels in this process, there is a greater ability for the machine to analyze the data to locate hidden structures contained within it. Unsupervised learning has become popular among those in the cybersecurity community.

Reinforcement Learning

Behavior modification involves the use of reward and penalty to encourage or discourage specific activities. For instance, if a dog is being house trained, it will be rewarded when it does its business outside and scolded when it does so inside. Reinforcement learning uses these same feedback responses to train machines to learn.

The algorithm for reinforcement learning is based on a trial and error model. Large amounts of data are input into the model, and the machine is rewarded or penalized subsequent to whether the selections help or hinder the objective of the application. Reinforcement learning is seen with the training of robots for industrial automation.

How Machine Learning Helps Humans

We see machine learning at work in our everyday lives. From our search engine results to our ride-sharing apps, machine learning is front and center in the process. What he have seen is that augmented intelligence enhances human’s intelligence. Let’s focus on some of the applications that use machine learning.

GPS has become a staple in the lives of all travelers. GPS uses machine learning to assist us in reaching our destination by using other users’ input and recognized patterns. Picture recognition used by Facebook is another form of machine learning that uses a supervised model. Ride-sharing apps use various machine learning models to predict destinations, estimate times, and determine pricing.

Conclusion

Once merely an element in sci-fi movies, AI has become a part of our daily experience. Whether we notice the presence of machine learning or not, our lives have been made notably simpler due to its role in developing applications designed to give humans the advantage. Hopefully, you can now recognize the ways that AI continues to benefit you every day.